Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/682
Title: On-line adaptive chaotic demodulator based on radial-basis-function neural networks
Authors: Feng, J
Tse, CKM 
Keywords: Chaotic demodulator
Henon map
Spread spectrum communication
Adaptive learning algorithm
Issue Date: 2001
Publisher: American Physical Society
Source: Physical review E, statistical, nonlinear, and soft matter physics, v. 63, no. 2 II, 2001, 026202, p. 1-10 How to cite?
Journal: Physical review E, statistical, nonlinear, and soft matter physics 
Abstract: Chaotic modulation is a useful technique for spread spectrum communication. In this paper, an on-line adaptive chaotic demodulator based on a radial-basis-function (RBF) neural network is proposed and designed. The demodulator is implemented by an on-line adaptive learning algorithm, which takes advantage of the good approximation capability of the RBF network and the tracking ability of the extended Kalman filter. It is demonstrated that, provided the modulating parameter varies slowly, spread spectrum signals contaminated by additive white Gaussian noise in a channel can be tracked in a time window, and the modulating parameter, which carries useful messages, can be estimated using the least-square fit. The Henon map is chosen as the chaos generator. Four test message signals, namely, square-wave, sine-wave, speech and image signals, are used to evaluate the performance. The results verify the ability of the demodulator in tracking the dynamics of the chaotic carrier as well as retrieving the message signal from a noisy channel.
URI: http://hdl.handle.net/10397/682
ISSN: 1063-651X
DOI: 10.1103/PhysRevE.63.026202
Rights: Copyright 2001 by the American Physical Society.
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